Title :
A globally optimized state-space model identification method
Author :
Liu, Lixian ; Han, Bingxin ; Li, Jinbo ; Li, Xinling
Author_Institution :
Dept. of Electr. & Electron. Eng., Shijiazhuang Railway Inst., Shijiazhuang
Abstract :
State space models are greatly favored by scientific researchers on account of particular superiority. Aiming at the minimized error criterion and using the matrix differential theory, the paper represented a global optimal state space model-identifying algorithm for stochastic state space model. This is a new identifying algorithm that integrates system parameters identification, structural identification and state estimation. In this method, hypothesis state space model is disturbed by the measuring noise and process noise. First, state space vector x is identified according to error criterion J(Es TEs). Then, system parameters matrix A, B, C and D is identified by criterion function J(Ei TEi). Results of mathematical simulation proved that this identifying method is characterized as simple calculation and higher identifying precision.
Keywords :
MIMO systems; matrix algebra; parameter estimation; state estimation; state-space methods; stochastic systems; MIMO system; hypothesis state space model; mathematical simulation; matrix differential theory; minimized error criterion; optimal state space model-identifying algorithm; parameter identification; state estimation; stochastic state space model; structural identification; Control systems; Control theory; MIMO; Mathematical model; Noise measurement; Optimization methods; Parameter estimation; State estimation; State-space methods; System identification; MIMO System; matrix calculation; state space; system identification;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593690